---
title: Deploy on cloud
sidebarTitle: Deploy on cloud
---
This guide shows you how to set up and use LangGraph Platform to deploy on cloud.

## Prerequisites

Before you begin, ensure you have the following:

* A [GitHub account](https://github.com/)
* A [LangSmith account](https://smith.langchain.com/) (free to sign up)

## 1. Create a repository on GitHub

To deploy an application to **LangGraph Platform**, your application code must reside in a GitHub repository. Both public and private repositories are supported. For this quickstart, use the [`new-langgraph-project` template](https://github.com/langchain-ai/react-agent) for your application:

1. Go to the [`new-langgraph-project` repository](https://github.com/langchain-ai/new-langgraph-project) or [`new-langgraphjs-project` template](https://github.com/langchain-ai/new-langgraphjs-project).
2. Click the `Fork` button in the top right corner to fork the repository to your GitHub account.
3. Click **Create fork**.

## 2. Deploy to LangGraph Platform

1. Log in to [LangSmith](https://smith.langchain.com/).
2. In the left sidebar, select **Deployments**.
3. Click the **+ New Deployment** button. A pane will open where you can fill in the required fields.
4. If you are a first time user or adding a private repository that has not been previously connected, click the **Import from GitHub** button and follow the instructions to connect your GitHub account.
5. Select your New LangGraph Project repository.
6. Click **Submit** to deploy.
  This may take about 15 minutes to complete. You can check the status in the **Deployment details** view.

## 3. Test your application in LangGraph Studio

Once your application is deployed:

1. Select the deployment you just created to view more details.
2. Click the **LangGraph Studio** button in the top right corner. [LangGraph Studio](/langgraph-platform/langgraph-studio) will open to display your graph.

## 4. Get the API URL for your deployment

1. In the **Deployment details** view in LangGraph, click the **API URL** to copy it to your clipboard.
2. Click the `URL` to copy it to the clipboard.

## 5. Test the API

You can now test the API:

<Tabs>
    <Tab title="Python SDK (Async)">
    1. Install the LangGraph Python SDK:
      ```shell
      pip install langgraph-sdk
      ```
    2. Send a message to the assistant (threadless run):
      ```python
      from langgraph_sdk import get_client

      client = get_client(url="your-deployment-url", api_key="your-langsmith-api-key")

      async for chunk in client.runs.stream(
          None,  # Threadless run
          "agent", # Name of assistant. Defined in langgraph.json.
          input={
              "messages": [{
                  "role": "human",
                  "content": "What is LangGraph?",
              }],
          },
          stream_mode="updates",
      ):
          print(f"Receiving new event of type: {chunk.event}...")
          print(chunk.data)
          print("\n\n")
      ```
    </Tab>
    <Tab title="Python SDK (Sync)">
    1. Install the LangGraph Python SDK:
      ```shell
      pip install langgraph-sdk
      ```
    2. Send a message to the assistant (threadless run):
      ```python
      from langgraph_sdk import get_sync_client

      client = get_sync_client(url="your-deployment-url", api_key="your-langsmith-api-key")

      for chunk in client.runs.stream(
          None,  # Threadless run
          "agent", # Name of assistant. Defined in langgraph.json.
          input={
              "messages": [{
                  "role": "human",
                  "content": "What is LangGraph?",
              }],
          },
          stream_mode="updates",
      ):
          print(f"Receiving new event of type: {chunk.event}...")
          print(chunk.data)
          print("\n\n")
      ```
    </Tab>
    <Tab title="JavaScript SDK">
    1. Install the LangGraph JS SDK
      ```shell
      npm install @langchain/langgraph-sdk
      ```
    2. Send a message to the assistant (threadless run):
      ```js
      const { Client } = await import("@langchain/langgraph-sdk");

      const client = new Client({ apiUrl: "your-deployment-url", apiKey: "your-langsmith-api-key" });

      const streamResponse = client.runs.stream(
          null, // Threadless run
          "agent", // Assistant ID
          {
              input: {
                  "messages": [
                      { "role": "user", "content": "What is LangGraph?"}
                  ]
              },
              streamMode: "messages",
          }
      );

      for await (const chunk of streamResponse) {
          console.log(`Receiving new event of type: ${chunk.event}...`);
          console.log(JSON.stringify(chunk.data));
          console.log("\n\n");
      }
      ```
    </Tab>
    <Tab title="Rest API">
    ```bash
    curl -s --request POST \
        --url <DEPLOYMENT_URL>/runs/stream \
        --header 'Content-Type: application/json' \
        --header "X-Api-Key: <LANGSMITH API KEY> \
        --data "{
            \"assistant_id\": \"agent\",
            \"input\": {
                \"messages\": [
                    {
                        \"role\": \"human\",
                        \"content\": \"What is LangGraph?\"
                    }
                ]
            },
            \"stream_mode\": \"updates\"
        }"
    ```
    </Tab>
</Tabs>

## Next steps

You have deployed an application using LangGraph Platform. Here are some other resources to check out:

* [LangGraph Studio overview](/langgraph-platform/langgraph-studio)
* [Deployment options](/langgraph-platform/deployment-options)
